Define, design, and supervise the technical architecture of solutions based on intelligent agents and LLMs, integrating tools such as LangChain, LlamaIndex, AutoGen, CrewAI, or equivalent frameworks.
Implement MCP (Model Context Protocol) and A2A (Agent-to-Agent) architectures to enable multi-agent coordination and autonomous flows within business environments.
Work with the MLOps team and execution environments that enable continuous agent updating and deployment, including memory management, context, and long-term planning.
Collaborate closely with product, UX, data, and backend teams to map business needs to intelligent agent architectures.

5 years of experience in artificial intelligence projects and 2 years in the implementation of autonomous agents or co-pilots.
Fluent technical English.
Experience working with business data in domains such as accounting, finance, payroll, billing, or ERP.
Experience working with vector stores (Chroma, Weaviate, Pinecone) and RAG architectures.
Handling frameworks such as LangChain, LlamaIndex, AutoGen, CrewAI, Semantic Kernel, or similar.
Practical knowledge of MCP and A2A protocols, use of tools, memory management, and conversation status.
Solid command of Python and experience with FastAPI, asyncio, Pydantic, and asynchronous architectures.
Knowledge of MLOps: CI/CD, Docker, Kubernetes, agent monitoring, and automated retraining.
Practical knowledge of other languages such as Golang, Java, or C# (.NET), especially in building high-performance components (Nice to Have).

AI Engineer

Key skills